BMC Medical Research Methodology (Feb 2007)

Selective attrition and bias in a longitudinal health survey among survivors of a disaster

  • Stellato Rebecca,
  • van der Velden Peter,
  • van den Berg Bellis,
  • Grievink Linda

DOI
https://doi.org/10.1186/1471-2288-7-8
Journal volume & issue
Vol. 7, no. 1
p. 8

Abstract

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Abstract Background Little is known about the response mechanisms among survivors of disasters. We studied the selective attrition and possible bias in a longitudinal study among survivors of a fireworks disaster. Methods Survivors completed a questionnaire three weeks (wave 1), 18 months (wave 2) and four years post-disaster (wave 3). Demographic characteristics, disaster-related factors and health problems at wave 1 were compared between respondents and non-respondents at the follow-up surveys. Possible bias as a result of selective response was examined by comparing prevalence estimates resulting from multiple imputation and from complete case analysis. Analysis were stratified according to ethnic background (native Dutch and immigrant survivors). Results Among both native Dutch and immigrant survivors, female survivors and survivors in the age categories 25–44 and 45–64 years old were more likely to respond to the follow-up surveys. In general, disasters exposure did not differ between respondents and non-respondents at follow-up. Response at follow-up differed between native Dutch and non-western immigrant survivors. For example, native Dutch who responded only to wave 1 reported more depressive feelings at wave 1 (59.7%; 95% CI 51.2–68.2) than Dutch survivors who responded to all three waves (45.4%; 95% CI 41.6–49.2, p p Conclusion Our results indicate that despite selective response, the complete case prevalence estimates were only somewhat biased. Future studies, both among survivors of disasters and among the general population, should not only examine selective response, but should also investigate whether selective response has biased the complete case prevalence estimates of health problems by using statistical techniques such as multiple imputation.